Summary of Plugh: a Benchmark For Spatial Understanding and Reasoning in Large Language Models, by Alexey Tikhonov
PLUGH: A Benchmark for Spatial Understanding and Reasoning in Large Language Models
by Alexey Tikhonov
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research presents PLUGH, a modern benchmark that assesses the abilities of Large Language Models (LLMs) for spatial understanding and reasoning. The benchmark consists of 5 tasks with 125 input texts extracted from 48 different games and representing 61 different non-isomorphic spatial graphs. The evaluation shows that while some commercial LLMs exhibit strong reasoning abilities, open-sourced competitors can demonstrate almost the same level of quality. However, all models still have significant room for improvement. The study identifies typical reasons for LLM failures and discusses possible ways to deal with them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about testing how well language models understand spatial things like maps and graphs. They created a test called PLUGH that uses 125 texts from different games to see if the models can reason about these spatial things. The results show that some commercial language models are really good, but open-source ones are almost as good too! However, they all still need improvement. The researchers found out why the models sometimes fail and gave ideas on how to fix them. |
